Abstract
Dynamic thermal simulation use is essential for studying complex building systems. One of the major challenges to widely deploy optimization in this field’ engineering and research is the high computational cost to perform the required simulation-based iterations. To participate in overcoming this hindrance, we propose in this paper an innovative time efficient Multiobjective Optimization methodology, based on coupling Latin Hypercube Design of Experiments to Artificial Neural Network polynomial regression and Genetic Algorithms (GA). This method allows to benefit from the robustness of Genetic Algorithms and from the rapidity of prediction of polynomial regression. RBD-FAST sensitivity Analysis indexes are also generated without any extra-time. We successfully applied the method to analyse and optimize a constrained 8-objectives problem with 13 input parameters in six climatic zones in Morocco.
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